Harnessing AI for Geospace Science: The AIMFAHR Project

Artificial Intelligence (AI) techniques, particularly Machine Learning (ML), have undergone significant growth in heliophysics research in recent years. Various ML models have emerged, some outperforming empirical and physics-based models while significantly reducing computational time. The Artificial Intelligence Modeling Framework for Advancing Heliophysics Research (AHR) project is an initiative aimed at integrating community-wide AI efforts into a unified AI modeling framework, advancing system-of-systems science in Sun-Earth interactions and enhancing the predictability of space weather hazards. This presentation highlights our initial efforts under the AIMFAHR project. We selected data-driven models of the magnetosheath, cusps, auroral precipitation, field-aligned currents (FACs), ionospheric electrodynamics, and thermospheric density as the initial set of AIMFAHR base models. As part of the MLGEM Challenge Storm study, we simulated geomagnetic storms on 4 Jan 2023, 6 May 2023, and 11 May 2024, selected by the Machine Learning-based Geospace Environment Modeling (MLGEM) resource group at the Geospace Environment Modeling (GEM) workshop. The AIMFAHR models reveal the storm responses of various geospace systems from a data-driven respective, including the spatiotemporal variation of the magnetopause reconnection line and its global dayside reconnection rate; cusp motions and the evolution of cusp ion energy dispersions; auroral boundary motions and variation in global auroral spectrums; increases in FACs and ionospheric potentials; and enhanced Joule heating in the upper atmosphere. These initial efforts provide valuable insights for future AIMFAHR activities, including ML model coupling, knowledge transfer between models, uncertainty quantification, and research-to-operation transitions.